Articles

This paper describes our system for participat- ing SemEval2013 Task2-B (Kozareva et al., 2013): Sentiment Analysis in Twitter. Given a message, our system classifies whether the message is positive, negative or neutral senti- ment. It uses a co-occurrence rate model. The training data are constrained to the data pro- vided …...

This paper presents our system, TJP, which participated in SemEval 2013 Task 2 part A: Contextual Polarity Disambiguation. The goal of this task is to predict whether marked con- texts are positive, neutral or negative. Howev- er, only the scores of positive and negative class will be used to calculate …...

In this paper, the UNITOR system participat- ing in the SemEval-2013 Sentiment Analysis in Twitter task is presented. The polarity de- tection of a tweet is modeled as a classifica- tion task, tackled through a Multiple Kernel approach. It allows to combine the contribu- tion of complex kernel functions, such …...

In this paper, we describe our system that par- ticipated in SemEval-2013, Task 2.B (senti- ment analysis in Twitter). Our approach con- sists of adapting Naive Bayes probabilities in order to take into account prior knowledge (represented in the form of a sentiment lex- icon). We propose two different methods …...

Named entity recognition (NER) systems are often based on machine learning tech- niques to reduce the labor-intensive devel- opment of hand-crafted extraction rules and domain-dependent dictionaries. Nevertheless, time-consuming feature engineering is often needed to achieve state-of-the-art performance. In this study, we investigate the impact of such domain-specific features on the …...

This paper presents the multi-phase relation extraction (RE) approach which was used for the DDI Extraction task of SemEval 2013. As a preliminary step, the proposed approach in- directly (and automatically) exploits the scope of negation cues and the semantic roles of in- volved entities for reducing the skewness in …...

The DDIExtraction 2013 task concerns the recognition of drugs and extraction of drug- drug interactions that appear in biomedical literature. We propose two subtasks for the DDIExtraction 2013 Shared Task challenge: 1) the recognition and classification of drug names and 2) the extraction and classification of their interactions. Both subtasks …...

This paper describes the systems submitted by Avaya Labs (AVAYA) to SemEval-2013 Task 2 - Sentiment Analysis in Twitter. For the constrained conditions of both the message polarity classification and contextual polarity disambiguation subtasks, our approach cen- ters on training high-dimensional, linear clas- sifiers with a combination of lexical and …...

This paper describes the details of our system submitted to the SemEval-2013 shared task on sentiment analysis in Twitter. Our approach to predicting the sentiment of Tweets and SMS is based on supervised machine learning tech- niques and task-specific feature engineering. We used a linear classifier trained by stochas- tic …...

We present two systems developed at the Uni- versity of Ottawa for the SemEval 2013 Task 2. The first system (for Task A) classifies the po- larity / sentiment orientation of one target word in a Twitter message. The second system (for Task B) classifies the polarity of whole Twitter …...

In this paper, we describe how we created two state-of-the-art SVM classifiers, one to de- tect the sentiment of messages such as tweets and SMS (message-level task) and one to de- tect the sentiment of a term within a message (term-level task). Among submissions from 44 teams in a competition, …...

In recent years, sentiment analysis in social media has attracted a lot of research interest and has been used for a number of applica- tions. Unfortunately, research has been hin- dered by the lack of suitable datasets, com- plicating the comparison between approaches. To address this issue, we have proposed …...

This paper describes our system for shared task 13 “Word Sense Induction for Graded and Non-Graded Senses” of SemEval-2013. The task is on word sense induction (WSI), and builds on earlier SemEval WSI tasks in ex- ploring the possibility of multiple senses be- ing compatible to varying degrees with a …...

Word sense induction aims to discover differ- ent senses of a word from a corpus by us- ing unsupervised learning approaches. Once a sense inventory is obtained for an ambiguous word, word sense discrimination approaches choose the best-fitting single sense for a given context from the induced sense inventory. However, …...

Most work on word sense disambiguation has assumed that word usages are best labeled with a single sense. However, contextual am- biguity or fine-grained senses can potentially enable multiple sense interpretations of a us- age. We present a new SemEval task for evalu- ating Word Sense Induction and Disambigua- tion …...

Our system combines text similarity measures with a textual entailment system. In the main task, we focused on the influence of lexical- ized versus unlexicalized features, and how they affect performance on unseen questions and domains. We also participated in the pi- lot partial entailment task, where our system significantly …...

Automatic scoring of short text responses to educational assessment items is a challeng- ing task, particularly because large amounts of labeled data (i.e., human-scored responses) may or may not be available due to the va- riety of possible questions and topics. As such, it seems desirable to integrate various approaches, …...

We present the results of the Joint Student Response Analysis and 8th Recognizing Tex- tual Entailment Challenge, aiming to bring to- gether researchers in educational NLP tech- nology and textual entailment. The task of giving feedback on student answers requires semantic inference and therefore is related to recognizing textual entailment. …...

We introduce Peripheral Diversity (PD) as a knowledge-based approach to achieve multi- lingual Word Sense Disambiguation (WSD). PD exploits the frequency and diverse use of word senses in semantic subgraphs de- rived from larger sense inventories such as BabelNet, Wikipedia, and WordNet in order to achieve WSD. PD’s f-measure scores …...

This article presents the GETALP system for the participation to SemEval-2013 Task 12, based on an adaptation of the Lesk measure propagated through an Ant Colony Algorithm, that yielded good results on the corpus of Se- meval 2007 Task 7 (WordNet 2.1) as well as the trial data for Task …...